Current state of the art indirect feature-based Visual-SLAM algorithms, such as ORB-SLAM2 [1]}, depend on the accuracy of the extracted features, such as ORB, and can fail in environments where feature matching is not robust. Direct SLAM methods such as LSD-SLAM [2]} operate directly on pixel-level brightness information and achieve tracking and pose estimation by minimizing the photometric error between the new frame and the current keyframe. However, direct methods are more susceptible to changes in lighting conditions, common in images taken in an agricultural field. Several works exist on object-level SLAM: SLAM++ [3]} is an RGB-D based system that detects known objects in the environment, which are then used as high-level landmarks. However, SLAM++ requires the creation of a database of high-quality 3D models used as prior. The authors in [4]} proposed a method for the online discovery of objects in indoor environments without the use of prior models. Each object is represented by the centroid of its associated pointcloud. However, only non-planar regions are considered as potential landmarks which limits the method's applicability in our setting: From the viewpoint of our camera, sorghum panicles appear to be mostly planar. In addition, our objective is to use individual seeds as landmarks and not the entire sorghum panicle. With the progress made in 2D object detection and segmentation, numerous works combine deep learning and SLAM: Fusion++ [5]} utilizes Mask-RCNN [6]} to initialize Truncated
Signed Distance Function (TSDF) reconstructions, which are, in turn, used as object-level representations of landmarks. Fusion++ mostly targets indoor environments.
QuadricSLAM [7]} and CubeSLAM [8]} are monocular-based SLAM systems. They propose two different 3D landmarks representations, “quadrics" and “cuboids," respectively, which are estimated from 2D bounding boxes without prior models. They are based on a factor-graph based SLAM formulation that jointly estimates the 3D pose of these objects along with the camera poses. The authors in [9]} explain that these approaches' performance will be poor in cluttered environments such as a sorghum field where there is a significant overlap between bounding boxes. Similarly, ROSHAN [10]} introduced an ellipsoid landmark-based monocular SLAM system which constraints the ellipsoid representation using information from detected bounding boxes, a prior on the shape of the object, and a “texture" plane estimated from triangulated feature points on the surface of the object. In our application, the overlap of the detected seeds' bounding boxes and the lack of texture in the images limit the applicability of such methods. Kimera [11]} uses Visual-Inertial Odometry to estimate the state of the robot and builds a semantic mesh model of the environment. We note that our system focuses on Stereo-Visual Odometry.
